The combination of recent advances in fluorescent probe technologies, automation of epifluorescent microscopy and image analysis has enabled high-content screening (HCS) to become a useful tool in the assessment of compound toxicity. For example, the detection of micronuclei (MN) in vitro may be used as a test of genotoxicity for biomonitoring, mutagenicity testing and to assess the proficiency of DNA-repair [1].
Currently, pharmaceutical genotoxicity units are governed by regulatory requirements and in vitro micronuclei tests can have a significant impact upon late stage developmental drugs where high costs have already been incurred. For example, the FDA/ICH currently require: i) a test for gene mutation in bacteria (Ames, or similar); ii) an in vitro test with cytogenetic evaluation of chromosomal damage (usually MN or chromosome aberration assays, MN may be used as a predicator of the mouse lymphoma assay) with mammalian cells or an in vitro mouse lymphoma assay; and iii) an in vivo test for chromosomal damage using rodent haematopoietic cells (in vivo bone marrow mouse MN). In vitro MN results can thus influence decisions regarding further downstream toxicity testing and entry into clinical trials, lead-modification or drug withdrawal.
Groups that are subject to regulatory approval adhere to guidelines and integrate a number of genotoxicity assays to ensure compliance and high confidence in detection sensitivity and specificity. Compounds are not progressed if there is evidence of genotoxicity even where it may be questionable and there is no knowledge of the interaction mechanism. Accuracy and precision of MN scoring are paramount to provide a sensitive, specific solution.
Whilst various conventional systems [2, 3] can help such groups with the screening processes, more current conventional systems and methods [4] may be highly dependent upon expert user input to help classify whether or not various image features are or are not, for example, micronuclei.
Other automated systems for classifying biological specimens are known in the prior art. Lee et al. [5] describes an automated microscope system comprising a computer and high speed processing field processor to identify free-lying cells. Long et al. [6] relates to algorithms that automatically recognise viable cells. The document discloses a method of identifying and localizing objects belonging to one of three or more classes, including deriving vectors, each being mapped to one of the objects, where each of the vectors is an element from an N-dimensional space. The method includes training an ensemble of binary classifiers with CISS technique, using training sets generated with an ECOC technique. Rutenberg [7] describes an automated cytological specimen classification system and method for increasing the speed and accuracy of cervical smear analysis. However, all of the above systems and methods [5, 6 and 7] are “pre-trained” in the sense that classification is based on pre-set criteria and thus the systems and methods do not improve with use.
Accordingly, there is a need to provide improved systems and methods that can more rapidly and accurately identify potential significant features of interest, for example, in an automated screening process or device. In particular, there is a need for improved systems and methods which are dynamically modifiable in the sense that they use algorithms which continuously learn, through for example human intervention or from other processors, and thus become more accurate with time.